Speckle noise (also called “speckle” in the following) is a granular noise that inherently exists in and degrades the quality of images obtained by active imaging devices, such as active radars, and synthetic aperture radars (SARs). Speckle noise in conventional radar results from random fluctuations in the return signal from an object that is no bigger than a single image-processing element. It increases the mean grey level of a local area.
Various techniques have been proposed to reduce the effect of speckle for illumination systems such as laser projectors, but these techniques concentrate on removing the phase coherence of the transmitted signal, which help to decrease the speckle on the final image. Such techniques however cannot be used for an active imaging device, since it is important that the transmitted signal in an active imaging device maintains its phase coherence.
Further, it has been proposed to use the wavelet transform to reduce the effects of the noise by thresholding the high frequency components. M. Matrinsi et al, “Fuzzy Thresholding in Wavelet Domain for speckle Reduction in Synthetic Aperture Radar Images”, International Journal of Intelligent Systems and Technologies, Summer 2006, p. 252-265 proposes to use a two dimensional (2D) discrete wavelet transform in the logarithmic domain in conjunction with a dynamic threshold which is controlled by a fuzzy controller. The wavelet transform is fixed and the examples show the performance with the Daubechies wavelet of order 15 is used. M. Matrinsi, A. E. Giraldez, “Smoothing of coefficients in wavelet domain for speckle reduction in synthetic Aperture Radar Images”, Journal of ICGST-GVIP, Volume 5, Issue 6, June 2005 proposes to use a two dimensional discrete wavelet transform which uses a smooth shrink threshold and a directional filtering approach. The wavelet used is fixed. Y. H. Lu., et al, “Speckle Reduction by Wavelet transform”, Microwave Conference 1999 Asia Pacific, Vol. 2, pp. 542-545 proposes to first use conventional filtering (in this case, Lee Filtering) and then use a 2D discrete wavelet transform using a soft threshold. The wavelet used is fixed and a transform based on the Daubechies wavelet of order 4 is used. Z. Zeng. et al, “Bayesian Speckle Noise Reduction Using the Discrete Wavelet Transform”, International Geo-science and Remote Sensing Symposium IGARSS '98, Seattle, 6-10 Jul., 1988 proposes to use a two-dimensional discrete wavelet transform in the logarithmic domain with multiple levels of decomposition. Bayesian estimation is then used to set the thresholds for the different levels. The wavelet used is fixed and a Daubechies wavelet of order 4 is used.